code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      groups=1,
      dcn_groups=1,
      is_vd_mode=False,
      act=None,
      is_dcn=False,
  ):
      super(ConvBNLayer, self).__init__()
  
      self.is_vd_mode = is_vd_mode
      self._pool2d_avg = nn.AvgPool2D(
          kernel_size=2, stride=2, padding=0, ceil_mode=True
      )
      if not is_dcn:
          self._conv = nn.Conv2D(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=kernel_size,
              stride=stride,
              padding=(kernel_size - 1) // 2,
              groups=groups,
              bias_attr=False,
          )
      else:
          self._conv = DeformableConvV2(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=kernel_size,
              stride=stride,
              padding=(kernel_size - 1) // 2,
              groups=dcn_groups,  # groups,
              bias_attr=False,
          )
      self._batch_norm = nn.BatchNorm(out_channels, act=act)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import ParamAttr
  import paddle.nn
  import paddle.nn.functional
  from paddle.vision.ops import DeformConv2D
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Normal, Constant, XavierUniform
label_desc: Use image node_id 1 to create a new ConvBNLayer object from inherited base classes: nn with example: obj = ConvBNLayer(in_channels, out_channels, kernel_size, stride, groups, dcn_groups, is_vd_mode, act, is_dcn)